General Prompt Engineering Guide for the OpenAI API

One-sentence takeaway

The guide presents reusable prompt engineering methods for message roles, the instructions parameter, context, examples, and formatting, and recommends using evaluation sets to validate prompt behavior after model upgrades.

Use cases

• Suitable tasks: Building API prompt templates, constraining output formats, and creating comparable regression evaluations for production prompts.

• Not suitable for: Treating general prompt engineering guidance as tuning advice specific to GPT-6 Luna.

• Applicable model versions: OpenAI API models. The example request uses gpt-6-astra; replace it with gpt-6-luna and evaluate it.

• Applicable clients, agents, or APIs: Responses API.

• Recommended reasoning effort and parameters: The page's example uses reasoning.effort: low; it does not claim this is optimal for Luna or for every task.

Ready-to-use content

Separate developer instructions from user input

The official example puts application rules in instructions and the current task in input:

import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
  model: "gpt-6-luna",
  reasoning: { effort: "low" },
  instructions: "Talk like a pirate.",
  input: "Are semicolons optional in JavaScript?",
});

console.log(response.output_text);

The example text and reasoning effort follow the source demonstration. Replace them with your own rules and input in a real application, and compare Luna's reasoning effort levels.

Use role messages

The page explains that the instructions parameter is roughly equivalent to putting developer rules in a developer message:

const response = await client.responses.create({
  model: "gpt-6-luna",
  reasoning: { effort: "low" },
  input: [
    {
      role: "developer",
      content: "Talk like a pirate.",
    },
    {
      role: "user",
      content: "Are semicolons optional in JavaScript?",
    },
  ],
});

console.log(response.output_text);

Version and evaluate production prompts

• Pin the model snapshot used in production to reduce behavior changes caused by model updates.

• Build tests and evaluation suites to monitor behavior as prompts change or model versions are upgraded.

• Put stable rules in instructions or a developer message; put the current task and data in a user message.

Reproduction notes and evidence limits

• The guide presents these methods as API prompt engineering practices; the page does not provide a Luna-specific comparative experiment.

• The model field in the examples above has been changed to gpt-6-luna for this task. The visible examples on the original page use gpt-6-astra, so this is a Luna adaptation using the official API format, not code the page provides verbatim for Luna.

• Before production deployment, compare prompt versions, reasoning effort, and expected output formats against the target input set. Do not judge stability from a single response.